The Application of Machine Vision Algorithm in Obstacle Recognition and Navigation of Logistics Robots

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. Through two sets of simulation experiments, the following conclusion can be drawn: compared with traditional methods, the comprehensive average improvement of obstacle accuracy index is about 11.5%, while the comprehensive average improvement of navigation efficiency index is 13.35%. This study injects innovative momentum into the logistics industry, enabling logistics robots to quickly and accurately perceive and navigate obstacles in complex environments, thereby enhancing their intelligence level in tasks such as warehousing and sorting, and making substantial contributions to improving logistics efficiency and reducing costs.